2,511 research outputs found
Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles
© 2016 IEEE. Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation
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Downloaded from ijr.sagepub.com at UNIV CALIFORNIA BERKELEY LIB on June 18, 2014Article Motion planning with sequential convex optimization and convex collision checkin
Planning for steerable needles in neurosurgery
The increasing adoption of robotic-assisted surgery has opened up the possibility to control innovative dexterous tools to improve patient outcomes in a minimally invasive way.
Steerable needles belong to this category, and their potential has been recognised in various surgical fields, including neurosurgery.
However, planning for steerable catheters' insertions might appear counterintuitive even for expert clinicians. Strategies and tools to aid the surgeon in selecting a feasible trajectory to follow and methods to assist them intra-operatively during the insertion process are currently of great interest as they could accelerate steerable needles' translation from research to practical use.
However, existing computer-assisted planning (CAP) algorithms are often limited in their ability to meet both operational and kinematic constraints in the context of precise neurosurgery, due to its demanding surgical conditions and highly complex environment.
The research contributions in this thesis relate to understanding the existing gap in planning curved insertions for steerable needles and implementing intelligent CAP techniques to use in the context of neurosurgery.
Among this thesis contributions showcase (i) the development of a pre-operative CAP for precise neurosurgery applications able to generate optimised paths at a safe distance from brain sensitive structures while meeting steerable needles kinematic constraints; (ii) the development of an intra-operative CAP able to adjust the current insertion path with high stability while compensating for online tissue deformation; (iii) the integration of both methods into a commercial user front-end interface (NeuroInspire, Renishaw plc.) tested during a series of user-controlled needle steering animal trials, demonstrating successful targeting performances. (iv) investigating the use of steerable needles in the context of laser interstitial thermal therapy (LiTT) for maesial temporal lobe epilepsy patients and proposing the first LiTT CAP for steerable needles within this context.
The thesis concludes with a discussion of these contributions and suggestions for future work.Open Acces
A Novel Flexible and Steerable Probe for Minimally Invasive Soft Tissue Intervention
Current trends in surgical intervention favour a minimally invasive (MI) approach,
in which complex procedures are performed through increasingly small incisions.
Specifically, in neurosurgery, there is a need for minimally invasive keyhole access,
which conflicts with the lack of maneuverability of conventional rigid instruments. In
an attempt to address this fundamental shortcoming, this thesis describes the concept
design, implementation and experimental validation of a novel flexible and steerable
probe, named “STING” (Soft Tissue Intervention and Neurosurgical Guide),
which is able to steer along curvilinear trajectories within a compliant medium.
The underlying mechanism of motion of the flexible probe, based on the reciprocal
movement of interlocked probe segments, is biologically inspired and was
designed around the unique features of the ovipositor of certain parasitic wasps.
Such insects are able to lay eggs by penetrating different kinds of “host” (e.g. wood,
larva) with a very thin and flexible multi-part channel, thanks to a micro-toothed
surface topography, coupled with a reciprocating “push and pull” motion of each segment.
This thesis starts by exploring these foundations, where the “microtexturing”
of the surface of a rigid probe prototype is shown to facilitate probe insertion into
soft tissue (porcine brain), while gaining tissue purchase when the probe is tensioned
outwards. Based on these findings, forward motion into soft tissue via a reciprocating
mechanism is then demonstrated through a focused set of experimental trials in
gelatine and agar gel. A flexible probe prototype (10 mm diameter), composed of
four interconnected segments, is then presented and shown to be able to steer in a
brain-like material along multiple curvilinear trajectories on a plane. The geometry
and certain key features of the probe are optimised through finite element models,
and a suitable actuation strategy is proposed, where the approach vector of the tip is
found to be a function of the offset between interlocked segments. This concept of a
“programmable bevel”, which enables the steering angle to be chosen with virtually
infinite resolution, represents a world-first in percutaneous soft tissue surgery.
The thesis concludes with a description of the integration and validation of a fully
functional prototype within a larger neurosurgical robotic suite (EU FP7 ROBOCAST),
which is followed by a summary of the corresponding implications for future
work
Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation
We present an error-controlled mesh refinement procedure for needle insertion
simulation and apply it to the simulation of electrode implantation for deep
brain stimulation, including brain shift. Our approach enables to control the
error in the computation of the displacement and stress fields around the
needle tip and needle shaft by suitably refining the mesh, whilst maintaining a
coarser mesh in other parts of the domain. We demonstrate through academic and
practical examples that our approach increases the accuracy of the displacement
and stress fields around the needle without increasing the computational
expense. This enables real-time simulations. The proposed methodology has
direct implications to increase the accuracy and control the computational
expense of the simulation of percutaneous procedures such as biopsy,
brachytherapy, regional anesthesia, or cryotherapy and can be essential to the
development of robotic guidance.Comment: 21 pages, 14 figure
Real-time Error Control for Surgical Simulation
Objective: To present the first real-time a posteriori error-driven adaptive
finite element approach for real-time simulation and to demonstrate the method
on a needle insertion problem. Methods: We use corotational elasticity and a
frictional needle/tissue interaction model. The problem is solved using finite
elements within SOFA. The refinement strategy relies upon a hexahedron-based
finite element method, combined with a posteriori error estimation driven local
-refinement, for simulating soft tissue deformation. Results: We control the
local and global error level in the mechanical fields (e.g. displacement or
stresses) during the simulation. We show the convergence of the algorithm on
academic examples, and demonstrate its practical usability on a percutaneous
procedure involving needle insertion in a liver. For the latter case, we
compare the force displacement curves obtained from the proposed adaptive
algorithm with that obtained from a uniform refinement approach. Conclusions:
Error control guarantees that a tolerable error level is not exceeded during
the simulations. Local mesh refinement accelerates simulations. Significance:
Our work provides a first step to discriminate between discretization error and
modeling error by providing a robust quantification of discretization error
during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM
Feedback Synthesis for Controllable Underactuated Systems using Sequential Second Order Actions
This paper derives nonlinear feedback control synthesis for general control
affine systems using second-order actions---the needle variations of optimal
control---as the basis for choosing each control response to the current state.
A second result of the paper is that the method provably exploits the nonlinear
controllability of a system by virtue of an explicit dependence of the
second-order needle variation on the Lie bracket between vector fields. As a
result, each control decision necessarily decreases the objective when the
system is nonlinearly controllable using first-order Lie brackets. Simulation
results using a differential drive cart, an underactuated kinematic vehicle in
three dimensions, and an underactuated dynamic model of an underwater vehicle
demonstrate that the method finds control solutions when the first-order
analysis is singular. Moreover, the simulated examples demonstrate superior
convergence when compared to synthesis based on first-order needle variations.
Lastly, the underactuated dynamic underwater vehicle model demonstrates the
convergence even in the presence of a velocity field.Comment: 9 page
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